Understand the model
IRIS reads the workbook and explains the drivers, assumptions, and financial relationships.
The AI harness for fundamental analysts.
Turn the Excel models you already use into an active research system.
IRIS understands how each forecast is built, puts your approach behind each estimate into the model with =AI(), and can rerun the approved analysis overnight as evidence and market conditions change.
Research, assumptions, model changes, and results stay connected—so you can see what moved, understand why, and know where to focus across the book.
Case Study: Where does AI-related debt exposure sit?
Put your approach into the forecast
=AI() puts your approach behind each estimate inside the Excel model you already use.
Start with what changed
IRIS can refresh approved inputs and rerun the affected analysis overnight, preserving the results for review.
Bring your model / IRIS reads the workbook
Start with the model you already have.
Import the Excel workbook. IRIS identifies the historical periods, forecast periods, operating drivers, assumptions, and financial statements—and explains how they fit together.
IRIS works out the spreadsheet once, so every AI question can start with the business.
How the model works
Follow the drivers through the financial statements.
See how volume and pricing build revenue, how margins turn sales into earnings, and how investment and working capital affect cash flow and financing needs.
IRIS reads the formulas and assumptions already in the workbook. Its explanation gives you a starting point for asking what matters and where to look more closely.
Understand the forecast
Know what sits behind each estimate.
What drives the number? Which assumptions matter most? What evidence supports the view, and where is the model thin?
Follow an estimate back to its drivers and supporting research. Keep that understanding with the model as the forecasts change.
Your approach / Inside Excel
Build your approach into every estimate.
Every important estimate reflects an approach: which evidence matters, which assumptions hold, and what should cause the forecast to move.
IRIS makes that approach explicit and puts it into Excel with =AI(). The estimate remains part of the workbook and flows through every calculation that depends on it.
=AI()
Your approach to the estimate, running inside the model.
Draft it with AI or write it yourself. Review it before it shapes the estimate.
Do the research / Beside the workbook
Research the company beside the forecast.
Ask what supports an assumption, what changed in the latest filing, where the model is thin, or what would change the investment view.
What supports this margin assumption?
What does the latest filing say about funding needs?
Which part of the forecast looks least supported?
What would make me change this estimate?
IRIS answers with the model already understood. Useful conclusions stay with the company and estimate instead of disappearing into another chat.
Test what changes
Test a different view.
Change a market assumption or challenge the company outlook. See what would move before approving it, then run the scenario through the actual workbook.
Ask, “What happens if SOFR rises 100 basis points?”
Review the assumptions
See what would change before you approve it.
IRIS finds where SOFR enters the forecast, shows which assumptions and estimates would be affected, and previews the proposed scenario.
See the financial impact
Run the view through the model.
After the analyst approves it, IRIS runs the scenario through the model and shows the effect on interest expense, earnings, cash flow, and financing needs.
The baseline stays intact. The scenario and its results remain available for comparison.
Overnight analysis / Morning review
Let the models keep working.
IRIS can refresh approved market and economic inputs overnight, identify which estimates depend on what changed, and rerun the affected analysis.
Start with what changed.
What moved?
Which forecasts held?
Which companies deserve attention?
What changed in earnings, cash flow, or financing?
Why?
The results stay available for review, with the assumptions, evidence, and prior work behind them. Begin the day knowing where to focus.
PM View / See the whole book
See the whole book.
Turn your models into an ecosystem.
The same move in rates, spreads, demand, or pricing can affect every company differently. IRIS lets the PM ask one question across the models and follow every answer back to the relevant forecast, assumptions, evidence, and prior work.
When the world changes, which of my models should change with it—and why?
A move in credit spreads may matter to funding cost in one company, reinvestment yield in another, and expected losses somewhere else.
Compare how the same economic change affects different companies, using the assumptions and forecasts in each model.
Research in one name can inform the next. Compare exposures, test a shared scenario, and find the evidence behind a view across the companies you cover.
Where are higher rates hurting earnings?
Which companies need refinancing?
Where would wider spreads improve returns?
Which estimates depend on assumptions that have just moved?
The team's research becomes knowledge the whole book can use.
Keep the history
Know why the numbers changed.
A revised estimate should not erase the view that came before it. IRIS keeps the approach behind the estimate, evidence, model change, and resulting financial impact together.
Go back to any version and see what changed, why it changed, who approved it, and how the change flowed through the model.
Across the book / Inside the model
Case Study: Where does AI-related debt exposure sit?
Start with CoreWeave (CRWV). Trace the financing obligations, ask who bears the exposure, and examine what would change its value. Each answer sets up the next question, from the scheduled debt to the evidence needed for an investment view.
The inquiry starts with evidence already in the model: lease obligations, the cash interest rate forecast, delayed-draw term loan (DDTL) and OEM financing disclosures, undrawn borrowing capacity, and equity. From there, it asks whether the exposure can be connected to other companies in the book.
A useful finding from the inquiry: in the exchange below, IRIS identifies the gap between forecasting contractual debt payments and estimating a DDTL's market value. It identifies what is still needed: which loan is being valued and as of when, its expected cash flows, market pricing, contractual terms, and credit risks. The next question asks how to resolve each gap.
Follow the inquiry: 11 questions from obligations to an investment thesis
Where the debt sits
- Which financing obligations does this model schedule explicitly, and which does it only name from the filings without a row?
- For the delayed-draw term loans, what committed, drawn and undrawn amounts does the available evidence establish, and as of what date?
- How does the model treat the OEM and software financing, and is any of it double-counted with the disclosed debt total?
- What does the model assume about lease liabilities versus lease payments, and where would a committed GPU lease show up?
Who bears it
- For DDTL 3.0, who is borrower, guarantor, arranger, disclosed lender and known current holder, and which of those roles has a date attached?
- Which of those lenders is a public company in the book, and what would be needed to connect this facility to that lender's model?
How its value changes
- What drives the cash interest rate assumption, and what happens to interest expense if SOFR moves 100 bp?
- Which obligations reprice with market rates and which are fixed, and does the model separate them?
- What would have to be true about revenue or utilization for the scheduled repayments to be covered from operations rather than refinancing?
Where the model is thin
- What evidence is missing before this model could support a mark on the DDTL, and which of those items are in the filings already available with the models?
- Which of the unknowns you named would a saved note or a revised approach to the estimate resolve, and which need new evidence?
Carry the conclusion forward. Save the conclusion as a note with the model. The next time the financing question comes up, the analyst can return to the evidence, interpretation, and unresolved questions.
AI for the investment question
Keep the AI focused on the economics.
Use Claude or Codex to research the company, challenge an assumption, or reason through a forecast. IRIS gives the AI an understood financial model and keeps the resulting work attached to it.
With the workbook already understood, the research can focus on what drives the estimate: funding costs, unit growth, margins, credit losses, pricing, or operating leverage.
Your questions build on earlier research and the current forecast. You can see the assumptions behind a proposed change, decide whether to use it, and return to the reasoning later.
From one model to the whole book
An active investment-research system.
Understand the models you already use.
Put your approach to each estimate inside them.
Rerun the approved analysis as the evidence changes.
Remember what happened. See where to focus across the book.
IRIS turns a collection of Excel models into an active investment-research system. The model stays in Excel, the analyst decides the approach, and each new question builds on the work that came before it.